Pi School’s Post

Day 2: Embeddings, Vector Databases, and Text Similarity 🧠🔍 Today's Highlights: 1.) Understanding Embeddings: Dove deeper into the conceptual foundations of embeddings and how they represent text as numerical vectors. 2.) Vector Databases and Retrieval: Explored the role of vector stores and databases in bringing live or specialist data into LLM applications. 3.) Text Similarity Workflows: learned techniques to leverage embeddings for classifying and comparing textual data. Hands-On Practice: >> Built a RAG (Retrieval-Augmented Generation) question-answering system over custom documents >> Experimented with text similarity using embeddings on Kaggle >> Constructed a neural classification network with Keras, utilizing embedded Key Learnings: 1.) Embeddings Power: Understand how embeddings capture semantic relationships between words and documents, enabling powerful text understanding. 2.) Vector Databases: learned how these specialized databases allow for efficient storage and retrieval of embedding-based data. 3.) Text Similarity Workflows: Explored techniques to measure and apply text similarity, from classification to recommendation systems. Essential Concepts: >> Embedding Algorithms (e.g., Word2Vec, GloVe, BERT) >> Approximate Nearest Neighbor (ANN) Search >> Cosine Similarity, Euclidean Distance >> Transfer Learning with Pretrained Embeddings Prompting Techniques: >> Retrieval-Augmented Generation (RAG) >> Embedding-based Information Retrieval Looking Ahead to Day 3: Tomorrow, we'll dive into the intriguing world of multimodal AI, exploring how we can leverage both text and visual data to unlock new capabilities. Can't wait to see what insights and hands-on skills await! Let me know if you have any other questions about today's learnings or the 5-Day Gen AI Intensive. I'm happy to discuss further! #GenerativeAI, #AITraining, #MachineLearning, #PromptEngineering, #AIInnovation, #LLM, #DataScience, #Kaggle, #AICommunity, #TechLearning, #Embeddings, #VectorDatabases, #TextSimilarity

Scaling Laws for Pre-Training Agents and World Models The paper explores how scaling (i.e., increasing model parameters, dataset size, and compute resources) affects the performance of embodied AI agents, specifically in behavior cloning and world modeling tasks. 🔹Introduction and Motivation The research aims to understand the impact of scaling on pre-trained embodied agents, similar to how scaling laws have been studied extensively for large language models (LLMs). ◾◾The focus is on two tasks: ◾World Modeling (WM): Learning to predict future observations based on past sequences. ◾Behavior Cloning (BC): Learning to mimic actions taken by humans or agents in specific environments. 🔹Scaling Laws ◾◾The paper demonstrates that scaling laws similar to those in LLMs apply to both world modeling and behavior cloning. ◾◾Power Laws are used to establish relationships between model size, dataset size, and training compute (FLOPs). These power laws allow researchers to predict the optimal model and dataset sizes for a given compute budget. 🔹Key Findings ◾◾World Modeling: The study finds that optimal scaling in world modeling depends heavily on the tokenizer used. A tokenizer with a higher compression rate (more tokens per observation) shifts the optimal trade-off toward larger model sizes. ◾◾Behavior Cloning: ◾When using tokenized input observations, the scaling laws skew towards favoring more data over larger models. ◾However, when using a CNN-based architecture (i.e., where observations are encoded as continuous embeddings), the scaling laws shift towards favoring larger models over more data. 🔹 Conclusion ◾◾This study establishes that scaling laws for pre-training can be extended beyond language models to embodied AI tasks like world modeling and behavior cloning. ◾◾The findings help optimize resource allocation for training agents in complex environments, providing guidelines on how to balance model size and dataset size. #GenAI #AI #LLM #datascience #machinelearning #Scaling #RAG #CNN Reference : https://lnkd.in/gkFeJacf

 AI DAILY NEWS - 5/5/2024 - GPT-5 Surpasses Predecessors: OpenAI's CEO Sam Altman assures that GPT-5 will outshine GPT-4, setting a new standard for AI intelligence. - Med-Gemini Revolutionizes Healthcare: Google DeepMind's Med-Gemini is set to transform medical diagnosis with its advanced clinical reasoning and data processing capabilities. - ScrapeGraphAI Eases Data Collection: The new Python library ScrapeGraphAI uses LLMs to streamline web scraping, making data more accessible. - Stories on X Delivers Summarized News: X's new platform, powered by Grok AI, offers quick and concise news summaries for the time-conscious reader. 🌐 Dive into the future of AI with these groundbreaking developments that promise to redefine our interaction with technology and healthcare. Stay tuned for more updates! 🚀 #AIAdvancements #TechNews #Innovation #ArtificialIntelligence #HealthTech #DataScience #MachineLearning #FutureIsNow

🌟 Day 2: Embeddings & Vector Databases Yesterday, I embarked on an incredible deep dive into the world of embeddings and vector databases—the unsung heroes behind many Large Language Model (LLM) capabilities. From understanding the conceptual foundations of embeddings to exploring their real-world applications, it’s been an eye-opening journey. The trade-offs in designing these systems? Fascinating and essential for crafting efficient, scalable solutions! 🔍 Hands-On Highlights: Here’s what I explored in today’s code lab: ✳️ RAG-Based Question-Answering System I implemented a Retrieval-Augmented Generation (RAG) model to answer questions from a custom document set. It was amazing to see how embeddings enhanced the precision of document retrieval, enabling dynamic, contextually rich responses! ✨ Text Similarity Analysis Using various embedding techniques, I compared text inputs for similarity. The nuanced differences in embeddings highlighted the model’s ability to grasp subtle connections between seemingly different pieces of text—truly mind-blowing! 🛠️ Neural Classification with Keras Leveraging embeddings in Keras, I built a basic neural classification network. The embeddings added remarkable precision to text classification tasks, underscoring their importance in complex language modeling. 📊 Key Insights Today’s work reinforced how embeddings elevate LLMs by enabling precise information retrieval, text classification, and dynamic data connections. Combined with vector databases, they unlock endless possibilities for custom AI applications—definitely a game-changer! And of course, daily live streams with experts like Logan Kilpatrick, Lee Boonstra, Aliaksei Severyn, Majd Al Merey, Mohammadamin Barekatain, Daniel J. Mankowitz, Chuck Sugnet, and Abdellahi El Moustapha. continue to deepen our understanding of this transformative tech. Can’t wait to uncover more insights tomorrow—stay tuned! #GenAI #LLM #Embeddings #VectorDatabases #ArtificialIntelligence #RAG #MachineLearning #DataScience #Innovation Kaggle Google

Do you want to remember quickly which are the important algorithms of ML? Here is a brief and simple intro of ML algorithms. 𝐋𝐢𝐧𝐞𝐚𝐫 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧: A supervised algorithm used for predicting continuous values based on linear relationships between input and output. 𝐋𝐨𝐠𝐢𝐬𝐭𝐢𝐜 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧: Used for binary classification tasks, predicting probabilities and classifying outcomes as 0 or 1. 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐓𝐫𝐞𝐞𝐬: A flowchart-like structure used for classification and regression tasks, splitting data into subsets based on feature values. 𝐑𝐚𝐧𝐝𝐨𝐦 𝐅𝐨𝐫𝐞𝐬𝐭: An ensemble method combining multiple decision trees to improve accuracy and prevent overfitting. 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 𝐕𝐞𝐜𝐭𝐨𝐫 𝐌𝐚𝐜𝐡𝐢𝐧𝐞𝐬 (𝐒𝐕𝐌): A classification technique that finds the best hyperplane to separate classes in high-dimensional spaces. 𝐤-𝐍𝐞𝐚𝐫𝐞𝐬𝐭 𝐍𝐞𝐢𝐠𝐡𝐛𝐨𝐫𝐬 (𝐤-𝐍𝐍): A simple algorithm that classifies data points based on the majority label of their nearest neighbors. 𝐍𝐚𝐢𝐯𝐞 𝐁𝐚𝐲𝐞𝐬: A probabilistic classifier based on Bayes’ Theorem, assuming independence between predictors. 𝐊-𝐌𝐞𝐚𝐧𝐬 𝐂𝐥𝐮𝐬𝐭𝐞𝐫𝐢𝐧𝐠: An unsupervised learning algorithm that groups data into k clusters based on similarity. 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐚𝐥 𝐂𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 (𝐏𝐂𝐀): A dimensionality reduction technique that transforms data into fewer dimensions while preserving as much variance as possible. 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬: A set of algorithms designed to recognize patterns, particularly in deep learning, used for tasks like image and speech recognition. These algorithms form the foundation of machine learning and are essential for building a variety of models in real-world applications. #AI #MachineLearning #Algorithms #Linearregression #LogisticRegression #DecisionTree #RandomForest

I’ve been diving deep into the world of ChromaDB, an open-source vector database designed for high-performance AI and machine learning applications. To help others explore its capabilities, I’ve created a tutorial on how to get started with ChromaDB. Whether you're a data scientist, ML enthusiast, or just curious about vector databases, this tutorial will give you a solid foundation to leverage ChromaDB in your projects. Further reading and information: ChromaDB: https://lnkd.in/gArh2Jfh ChromaDB Embeddings: https://lnkd.in/g9FdJPGQ ChromaDB Integration: https://lnkd.in/gQUfxSs9 Check it out here: https://lnkd.in/g7czX3tF #AI #MachineLearning #DataScience #OpenSource #ChromaDB #GitHub #Tutorial

🚀 Mimicking Human-Like Classifcaton with TabPFN: A Leap in AI for Tabular Data One of the most fascinating aspects of TabPFN is its ability to infer patterns and make predictions from very little data during inference—a behavior strikingly similar to how humans learn and adapt! 💡 Why is this so exciting? Humans don’t need to be trained on thousands of examples to understand a new pattern. We can observe a few instances, grasp the underlying structure, and apply our understanding to new scenarios. TabPFN mirrors this by using in-context learning: It directly processes labeled examples and learns relationships dynamically. No need for retraining or fine-tuning—it adapts in real time to the task at hand. ✨ How Does It Achieve This? TabPFN relies on Prior-Data Fitted Networks (PFNs), trained on millions of synthetic datasets. This equips the model with a vast prior knowledge base, allowing it to generalize across tasks. During inference, it combines this pre-learned knowledge with the new data, ensuring fast and accurate predictions. ⚡ Why It’s Groundbreaking This human-like ability to classfy from limited data isn’t just efficient; it’s revolutionary for real-world problems where small datasets are common. From personalized healthcare to niche financial modeling, TabPFN makes state-of-the-art ML accessible where traditional methods struggle. 🌍 A Step Toward More Human-Centric AI By embedding such human-like learning behavior in AI systems, we’re not just solving technical challenges—we’re bridging the gap between how machines and humans understand the world. 📂 Try It Out! TabPFN is open-source and easy to integrate with your projects. Explore the GitHub repository here: https://lnkd.in/gGrAYUAK. Kudos to the researchers behind TabPFN for this brilliant innovation! 🧠✨ #AI #MachineLearning #TabularData #HumanLikeLearning #Innovation #AutoML #AIResearch

🚀 Excited to Share the Project: Building a Semantic Search and QA System! 🤖 Project that leverages some of the most advanced tools in AI and machine learning, including LangChain, ChromaDB, and Google Generative AI. This project is all about creating a semantic search and question-answering system that can process and retrieve information in a highly efficient and intelligent manner. 🔍 What’s Inside? Data Preparation: Load and process text documents to build a robust dataset. Text Splitting: Break down documents into manageable chunks for better processing. Vector Database: Use embeddings generated by Google Generative AI to create a searchable vector database with ChromaDB. Semantic Search: Perform powerful searches within the vector database to retrieve relevant information. QA Chain: Build a chain that not only retrieves information but also answers complex queries using Google Gemini. 🌟 Why This Project Matters As AI continues to evolve, the ability to retrieve and understand vast amounts of information quickly and accurately is becoming increasingly important. This project demonstrates how you can combine cutting-edge technologies to build systems that can perform complex tasks with ease. If you're interested in exploring how these technologies work together, or if you're looking for inspiration for your own projects, check out my GitHub repository: 🔗 GitHub Link: https://lnkd.in/d_f3eaFY #AI #MachineLearning #DataScience #LangChain #ChromaDB #GoogleGemini #SemanticSearch #OpenSource #TechInnovation

The 2024 Stanford State of AI Report is out: Another fantastic annual report from Stanford providing a comprehensive overview of the evolving landscape of the AI ecosystem. Some key highlights this year includes noting the significant dominance of AI development in industry, and the steeply rising costs of training advanced models like Google’s Gemini Ultra and OpenAI’s GPT-4. Other key findings highlight the United States' leadership in global AI development and investment, alongside a growing concern over the lack of standardization in responsible AI practices which complicates the evaluation of AI systems' safety and fairness. Overview: https://lnkd.in/ea7KF28 Visual Charts: https://lnkd.in/gK3T3Vue Full Report PDF: https://lnkd.in/dkrMSdqD -- If you liked this article you can join 60,000+ practitioners for weekly tutorials, resources, OSS frameworks, and MLOps events across the machine learning ecosystem: https://lnkd.in/eRBQzVcA #ML #MachineLearning #ArtificialIntelligence #AI #MLOps #AIOps #DataOps #augmentedintelligence #deeplearning #privacy #kubernetes #datascience #python #bigdata